Data Management Level 2

Question 1

How have you searched for comparable evidence?

Answer 1

• Internal leasing team operating in the area
• Internal database for completed transactions
• CoStar – speak to agents to verify data or to fill in any data gaps.

Question 2

What statics did you report and how did this help your client?

Answer 2

I reported on the key collection percentages, this helped my client in terms of their portfolio debt reporting and helped highlight if further action was needed against any tenant to recover costs.

Question 3

How did you filter the data?

Answer 3

I filtered the data to ensure only invoices raised in that quarter were included and removed any additional invoices that did not form part of the usual quarterly charges under the tenant’s leases.

Question 4

How did you streamline the reports?

Answer 4

I removed columns that were not relevant for my clients reporting so that they could clearly find the information they were looking for e.g. internal tenant system codes etc.

Question 5

How did you present the data?

Answer 5

A simplified excel table showing the full collection data for their portfolio, but only showing key columns such as the invoice type, due date, full invoice amount and remaining balance, with additional subtotals included for each asset and a column at the end expressing the remaining balance as a percentage. The table was also colour coded for clarity with green for paid, orange part-paid and red for outstanding.
I put the key statistics on a covering email.

Question 6

Why did you present in descending order?

Answer 6

This was in line with my clients’ reporting standards and presenting in descending order made it clear which tenant’s debt were a priority.

Question 7

What were the key statics?

Answer 7

Key statistics included the overall % collected each week from the due date for each charge type.

Question 8

What are the benefits and limitations to using secondary data (CoStar) and how did you overcome this?

Answer 8

Benefits include a much wider range of evidence to review
Disadvantages include missing data or potentially incorrect information reported. To overcome this I find the agents details that completed to transaction to verify the data and fill in any data gaps and only use the data if there is enough reliable evidence available.

Question 9

What characteristics did you consider?

Answer 9

• Location
• Tenure (inside/outside/FRI/IRI)
• Specification / condition / construction
• Transaction date
• Size
• Vacancy rate
• Covenant strength

Question 10

What are the risks of not verifying data?

Answer 10

• Data could be incorrect which could lead to a valuation based on incorrect evidence.

Question 11

Talk me through your multi-step validation process

Answer 11

• I reviewed the data found on Costar against the evidence I had established in-house and cross checked to establish was missing data I needed to confirm, or any irregularities, for instance if a floor area was reported in line with different measurement standards.
• I then searched the agents details that completed the transaction and contacted them to verify the data reported and request further information where I needed it.
• Once I had all of the facts I ensured I only used data similar to my property and did not include anomalies than could skew the data.

Question 12

What additional market insights did you uncover:

Answer 12

• Further information on the leasing side of the transaction, which helped when making adjustments to establish the market rent as it allowed me to factor in other important factors such as covenant strength, or incentives that were not reported online so that I could verify the rent.

Question 13

How did you organise the data?

Answer 13

I organised the data into a structured table clearly showing the details of each transaction and the adjustments made for each considered factor to clearly show how I decided on my market rent.
This was then inputted into the valuation report by my director and used to support their valuation.

Question 14

Why and how did you filter the database?

Answer 14

The raw data sheets from the government database can only be downloaded as entire Council’s and includes all EPC data including those that have expired. To cover the whole of Central London, I downloaded the separate data sheets for Southwark, Westminster and City of London. I then had to filter the data by postcode to remove EPC’s for places like Bermondsey that ae not considered to be ‘Central London’. I also filtered by Use Class, specifically B1 to align with my clients assets and ensured that any EPCS prior to August 2014 were removed as these had expired.

Question 15

What format was the data in?

Answer 15

The original data was a very large excel spreadsheet of raw data that was difficult to read and included masses of data.
I then filtered the data to include relevant data only and put this into simple tables showing the 5 year average and yearly increases from 2014-2024.

Question 16

How does COUNTIF function work?

Answer 16

It works by calculating how many of a particular selected work or number comes up in a pool of data. For this example I carried out the countif function for each EPC grading from A+ to G and for each year from 2014-2024.
COUNTIF(A1:A100 ‘A’)
- Type the word ‘COUNTIF’ in all capitals
- Highlight the column of data you want to select from
- Then in inverted commas, type the word/number you are looking for.
Once I had this for each year I put this into a table for each year and calculated the percentage of each rating used for each year to establish an average rating.

Question 17

What did the data tell you about the Central London market?

Answer 17

That the average rating across Central London was a ‘B’ with 57% of EPCs at a B as of August 2024.
Average was a ‘D’ in 2020 with 33% at a D.

Question 18

How did their portfolio compare?

Answer 18

Below average with an average rating of ‘C’

Question 19

How did you present your findings?

Answer 19

I consolidated the original three raw data sets into one table

Question 20

How do you define sensitive data?

Answer 20

RICS, or the Royal Institution of Chartered Surveyors, defines sensitive data as information that requires extra protection due to its potential to cause damage or distress if disclosed or misused, often falling under specific legal obligations like the GDPR. Examples include data revealing racial or ethnic origin, health information, or bank details, which necessitate strict controls on collection, processing, and access to prevent harm to individuals or organisations.

Question 21

What is enterprise-grade security:

Answer 21

Enterprise-grade security refers to a comprehensive, multi-layered strategy and set of tools designed to protect the entire information technology infrastructure of a large organisation (an “enterprise”). It is not a single product, but a holistic approach built to handle the scale, complexity, and high-stakes risk environment that large businesses face.

Question 22

What were the benefits of using an internal database:

Answer 22

• Security of data
• Access controls
• Audit trails.

Question 23

Are there any risks with using cloud servers?

Answer 23

Data breaches, misconfigurations, malware, denial-of-service (DoS) attacks, and data loss
For instance I’m aware that BOX had a data breach a few years ago and I’m aware that many companies, for instance Microsoft, have suffered from cyberattacks.

Question 24

Did you consider any other methods of transferring data, if so what?

Answer 24

I considered other cloud servers I have used previously including BOX and Dropbox but chose JLL’s Connect SharePoint as it is in line with my companies guidelines, allows access controls and can facilitate sending large amounts of data.